First, we load packages, set the Google API key, and define a palette used for visualizing traffic data in leaflet.
## Load Google Traffic package
library(googletraffic)
## Load packages for working with and visualizing data
library(leaflet)
library(leaflet.extras)
library(leaflet.providers)
library(scales)
library(mapview)
library(raster)
library(tidyverse)
## Set Google API Key
google_key <- "GOOGLE-API-KEY-HERE"
## Leaflet Palette
pal <- colorNumeric(c("green", "orange", "red", "#660000"),
1:4,
na.color = "transparent")The following are key parameters used when querying Google Traffic data.
height and
width parameters define the height and width of the raster
in terms of pixels. The kilometer height/width of pixels depends
primarily on the zoom level (larger zoom
levels correspond to the height and width
having a smaller kilometer distance). Google traffic data takes time to
render on a map, and larger height and width
values require more time needed for data to render. The functions
automatically scale the delay time depending on the height
and width values set, but the delay time can also be
manually set using the webshot_delay parameter. Note that
traffic data may fail to render for very large height and
width values, no matter the webshot_delay
set.The gt_make_raster() function produces a raster, using a
centroid location and a height/width around the centroid to specify the
location to query traffic information. The below example queries traffic
for lower Manhattan, NYC.
## Make raster
r <- gt_make_raster(location = c(40.712778, -74.006111),
height = 1000,
width = 1000,
zoom = 16,
google_key = google_key)
#> Pausing for 5 seconds to allow traffic data to render
## Map raster
leaflet(width = "100%") %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addRasterImage(r, colors = pal, opacity = 1)By using a smaller zoom, we can capture a larger area;
however, the pixels are more coarse.
## Make raster
r <- gt_make_raster(location = c(41.384900, -78.891302),
height = 1000,
width = 1000,
zoom = 7,
google_key = google_key)
#> Pausing for 5 seconds to allow traffic data to render
## Map raster
leaflet(width = "100%") %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addRasterImage(r, colors = pal, opacity = 1)The above example showed querying traffic information for lower
Manhattan. Here, we show querying traffic information for all of
Manhattan while still using a relatively high zoom level (that allows
capturing traffic on smaller streets). The
gt_make_raster_from_polygon() accepts a polygon as an
input; if needed, multiple API queries are made to query traffic for the
full polygon. We still specify the height and
width, which determines the height and width used for a
single API query. Large height and width
values will result in fewer Google queries, while smaller
height and width values will require more
queries to cover the same spatial area; traffic data will fail to render
if too large of height and width values are
set.
## Grab polygon of Manhattan
us_sp <- getData('GADM', country='USA', level=2)
ny_sp <- us_sp[us_sp$NAME_2 %in% "New York",]
## Make raster
r <- gt_make_raster_from_polygon(polygon = ny_sp,
height = 2000,
width = 2000,
zoom = 15,
google_key = google_key)
## Map raster
leaflet(width = "100%") %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addRasterImage(r, colors = pal, opacity = 1)gt_make_raster_from_polygon() creates a grid that covers
a polygon, creates a traffic raster for each grid, and merges the
rasters together. Some may prefer to first create and see the grid, then
create a traffic raster using this grid. For example, one could (1)
create a grid that covers a polygon then (2) remove certain grid tiles
that cover areas that may not be of interest. The
gt_make_grid() and gt_make_raster_from_grid()
functions facilitate this process; gt_make_grid() creates a
grid, then gt_make_raster_from_grid() uses a grid as an
input to create a traffic raster.
First, we create a grid using gt_make_grid().
grid_df <- gt_make_grid(polygon = ny_sp,
height = 2000,
width = 2000,
zoom = 15)
leaflet(width = "100%") %>%
addTiles() %>%
addPolygons(data = grid_df, popup = ~as.character(id))We notice that the tile in the bottom left corner just covers water and some land outside of Manhattan. To reduce the number of API queries we need to make, we can remove this tile.
grid_clean_df <- grid_df[-5,]
leaflet(width = "100%") %>%
addTiles() %>%
addPolygons(data = grid_clean_df)Second, we use the grid to make a traffic raster using
gt_make_raster_from_grid().
## Make raster
r <- gt_make_raster_from_grid(grid_param_df = grid_clean_df,
google_key = google_key)
## Map raster
leaflet(width = "100%") %>%
addProviderTiles("Esri.WorldGrayCanvas") %>%
addRasterImage(r, colors = pal, opacity = 1)To make a google traffic raster, the functions first makes a temporary png file then converts the png file to a raster—where only the raster is outputted. Some workflows may require separating the processes. For example, if querying Google traffic data on a regular basis on a server, a user with a small server may want to minimize the processes done on the server. Here, a user could create and save PNG files on a server, then download the PNG files and convert the PNGs to a raster locally.
To support these workflows, the package provides the: *
gt_make_png() function which creates a PNG
file with traffic data *
gt_load_png_as_traffic_raster() function
which converts a PNG file into a spatially-referenced traffic raster
The below example illutrates the process.
#### Make png
# The function does not output anything in R; it saves a png file, specified using the "out_filename" parameter
gt_make_png(location = c(40.712778, -74.006111),
height = 1000,
width = 1000,
zoom = 16,
out_filename = "google_traffic.png",
google_key = google_key)
#### Convert png to raster
# We now convert the "google_traffic.png" created above into a raster. Because the png is not spatially referenced, we need to enter the same
r <- gt_load_png_as_traffic_raster(filename = "google_traffic.png",
location = c(40.712778, -74.006111),
height = 1000,
width = 1000,
zoom = 16)We can also use this process when querying traffic data for a larger study area that requires making multiple API calls. The below example illustrates creating multiple pngs from a grid.
#### Make grid
# We first make a grid, which contains all the parameters needed to make a png then the raster
grid_df <- gt_make_grid(polygon = ny_sp,
height = 2000,
width = 2000,
zoom = 15)
print(grid_df)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -74.10988 ymin: 40.68462 xmax: -73.85324 ymax: 40.87875
#> Geodetic CRS: WGS 84
#> longitude latitude id height width zoom geometry
#> 1 -73.98156 40.84630 1 2000 2000 15 POLYGON ((-74.02448 40.8138...
#> 2 -73.89616 40.84630 2 2000 2000 15 POLYGON ((-73.93907 40.8138...
#> 3 -73.98156 40.78173 3 2000 2000 15 POLYGON ((-74.02448 40.7492...
#> 4 -73.89616 40.78173 4 2000 2000 15 POLYGON ((-73.93907 40.7492...
#> 5 -74.06696 40.71715 5 2000 2000 15 POLYGON ((-74.10988 40.6846...
#> 6 -73.98156 40.71715 6 2000 2000 15 POLYGON ((-74.02448 40.6846...#### Make PNGs from grid
for(i in 1:nrow(grid_df)){
grid_i_df <- grid_df[i,]
gt_make_png(location = c(grid_i_df$latitude, grid_i_df$longitude),
height = grid_i_df$height,
width = grid_i_df$width,
zoom = grid_i_df$zoom,
out_filename = paste0(i, "_google_traffic.png"),
google_key = google_key)
}
#### Convert PNGs to rasters
# Here we make a list of rasters
r_list <- lapply(1 in 1:nrow(grid_df)){
grid_i_df <- grid_df[i,]
gt_load_png_as_traffic_raster(filename = paste0(i, "_google_traffic.png"),
location = c(grid_i_df$latitude, grid_i_df$longitude),
height = grid_i_df$height,
width = grid_i_df$width,
zoom = grid_i_df$zoom)
}
#### Mosaic rasters together
# To mosaic the rasters together, the mosaic() function from the raster package requires that rasters have the same origin and resolution. The above rasters will not have the same orgin, and the resolutions will be slightly different. The gt_mosaic() function allows mosaicing rasters with different origins and resolutions.
r <- gt_mosaic(r_list)